(General Hire)Machine Learning Engineer Graduate (TikTok Recommendation) - 2027 Start

We are looking for talented individuals to join our team. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth.

Successful candidates must be able to commit to an onboarding date by the end of the year. Please state your availability and graduation date clearly in your resume.

Candidates can apply to a maximum of two positions and will be considered for jobs in the order you apply. The application limit is applicable to our Company and its affiliates' jobs globally. Applications will be reviewed on a rolling basis - we encourage you to apply early.

• Build industry-leading recommendation system, improving user experience, content ecosystem and platform security;
• Deliver end-to-end machine learning solution to address critical product challenges;
• Own the full stack machine learning system and optimize algorithms and infrastructure to improve recommendation performance.
• Work with cross functional teams to design product strategies and build solutions to grow TikTok in important markets.

• Individuals who are completing or have recently completed a Bachelor's/ Master's degree in computer science or a related discipline.
• Proficient coding skills and strong algorithm & data structure using C++/Python or other programming language
• Experienced in Machine Learning, familiar with at least one DeepLearning framework, e.g. PyTorch, Tensorflow.
• Experienced in one or more of the following areas, e.g. NLP, CV, Recommender System, and Machine Learning, etc
• Effective communication and teamwork skills

• Authors of published papers in accredited academia conferences is an advantage
• Winners of algorithm and machine learning competitions such as ACM and Kaggle is an advantage

Recommendation algorithm team plays a central role in the company, driving critical product decisions and platform growth. The team is made up of machine learning researchers and engineers, who support and innovate on production recommendation models and drive product impact. The team is fast-pacing, collaborative and impact-driven.